36 resultados para Discriminative Itemsets


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Background: Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods: Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results: This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion: The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

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This study proposes the application of fractal descriptors method to the discrimination of microscopy images of plant leaves. Fractal descriptors have demonstrated to be a powerful discriminative method in image analysis, mainly for the discrimination of natural objects. In fact, these descriptors express the spatial arrangement of pixels inside the texture under different scales and such arrangements are directly related to physical properties inherent to the material depicted in the image. Here, we employ the Bouligand-Minkowski descriptors. These are obtained by the dilation of a surface mapping the gray-level texture. The classification of the microscopy images is performed by the well-known Support Vector Machine (SVM) method and we compare the success rate with other literature texture analysis methods. The proposed method achieved a correctness rate of 89%, while the second best solution, the Co-occurrence descriptors, yielded only 78%. This clear advantage of fractal descriptors demonstrates the potential of such approach in the analysis of the plant microscopy images.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The purpose of this study is to develop and validate a dissolution test for fluconazole, an antifungal used for the treatment of superficial, cutaneous, and cutaneomucous infections caused by Candida species, in capsules dosage form. Techniques by HPLC and UV first derivative spectrophotometry (UV-FDS) were selected for quantitative evaluation. In the development of release profile, several conditions were evaluated. Dissolution test parameters were considered appropriate when a most discriminative release profile for fluconazole capsules was yielded. Dissolution test conditions for fluconazole capsules were 900 mL of HCl 0.1 M, 37 ± 0.5 °C using baskets with 50 rpm for 30 min of test. The developed HPLC and UV-FDS methods for the antifungal evaluation were selective and met requirements for an appropriate and validated method, according to ICH and USP requirements. Both methods can be useful in the registration process of new drugs or their renewal. For routine analysis application cost, simplicity, equipment, solvents, speed, and application to large or small workloads should be observed.

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Pós-graduação em Psicologia do Desenvolvimento e Aprendizagem - FC

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Pós-graduação em Psicologia do Desenvolvimento e Aprendizagem - FC